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Design of controller for the process operation is typically based on the availability of the best model. Development of empirical model is incorporated with many assumptions and approximations. System identification is developed to alleviate this problem by considering the input and output data of the process for the model development. The cascaded tank process is highly nonlinear and non-minimum phase process and often its working conditions are variable. Due to inadequacy of linear system identification to capture the dynamic of process, nonlinear system identification is incorporated. Identification of Cascaded two tank process using wiener model is reported. Wiener model parameters are estimated using recursive prediction error method.

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